Safety analysis and control of a robotic spinal surgical system

Screw path drilling is one of the most common and high-risk operations in many kinds of orthopedic surgery, especially in spinal surgeries. During spinal surgery, the bone screws are inserted into the vertebral body from the narrow vertebral pedicles. Any failures in this process will hurt important vessels and nerves of the patient. In this paper two aspects of the safety issues with using the Robotic Spinal Surgery System (RSSS) are analyzed: movement control and real-time operation control. For the safety motion control of the RSSS, two modes are developed: the cooperative control mode for positioning and the fine adjustment mode for precisely adjusting orientation. An automatic calibration algorithm for force/torque sensors is proposed to eliminate gravity effects. Guidance Virtual Fixtures (GVFs) and Forbidden Region Virtual Fixtures (FRVFs) are used to limit the movement of the RSSS. Damping Region Virtual Fixtures (DRVFs) are proposed to prevent the RSSS from crossing the constraint surface and harming the patient’s body. In the path drilling process, a state recognition algorithm is proposed to simulate the feeling in the hand of the surgeon during surgery. Based on force feature extraction and state recognition algorithm, 5 states in the drilling process are recognized, and the control point, which is the stop point of drilling, is found. Experiments are carried out to verify the DRVFs effects in the motion control of RSSS, the state recognition and safety control of the pedicle drilling.     

模糊理论在数控铣床加工中的应用研究

基于对数控铣床加工过程智能优化与提高加工效率的目的,改变传统的数控铣床加工过程中加工参数一成不变情况。本文从加工过程中,人的思维逻辑作用出发,选定模糊理论作为调整加工参数的方法设计一个能够自动调整伺服电机进给速度的模糊控制器,实现伺服电机的恒功率加工,提高铣削加工效率。并结合仿真手段对模糊控制器的控制作用进行仿真分析。找到一个合适的进给速度,对模糊控制器在数控铣床实际加工中的应用提供一个参数指导。     

Fuzzy Impulsive Control of High-Order Interpolative Low-Pass Sigma–Delta Modulators

In this paper, a fuzzy impulsive control strategy is proposed. The state vectors that the impulsive controller resets to are determined so that the state vectors of interpolative low-pass sigma–delta modulators (SDMs) are bounded within any arbitrary nonempty region no matter what the input step size, the initial condition and the filter parameters are, the occurrence of limit cycle behaviors and the effect of audio clicks are minimized, as well as the state vectors are close to the invariant set if it exists. To work on this problem, first, the local stability criterion and the condition for the occurrence of limit cycle behaviors are derived. Second, based on the derived conditions, as well as a practical consideration based on the boundedness of the state variables and a heuristic measure on the strength of audio clicks, fuzzy membership functions and a fuzzy impulsive control law are formulated. The controlled state vectors are then determined by solving the fuzzy impulsive control law. One of the advantages of the fuzzy impulsive control strategy over the existing linear control strategies is the robustness to the input signal, the initial condition and the filter parameters, and that over the existing nonlinear control strategy are the efficiency and the effectiveness in terms of lower frequency of applying the control force and higher signal-to-noise ratio (SNR) performance.     

Fuzzy logic for constant force control of end milling

The machining condition usually has significant variation resulting from the change of cutting depth and the intrinsic property of the workpiece. In order to maintain the performance of a classical proportional integral derivative control system, the tool life and machining quality, conservative feedrate, and cutting depth change are prespecified as the limitations of computer numerically controlled operators. Therefore, constant cutting force control is proposed as a useful approach for increasing the metal removal rate and the tool life. However, the model-based controller cannot handle the nonlinearity of a force control system due to cutting condition variations. Here, a fuzzy controller with learning ability was employed to improve both the system performance and the adaptability. This control approach vias implemented on a retrofit old-fashioned milling machine for the end milling process. The experimental results show that this control strategy has smooth feedrate and good cutting force dynamic responses     

Detection of Replica Node Attack Based on Exponential Moving Average Model in Wireless Sensor Networks

Due to the broadcast nature of wireless communication, wireless sensor networks (WSNs) are susceptible to several attacks. Amongst them, replica attack is one of the predominates as it facilitates the attackers to perform some other attacks. So, it is of immense significance to design a competent security method for WSNs. Introducing a trust method is the primary concern for assisting well-organized use of the available energy in each node in the energy restricted environment. In order to tradeoff between energy usage and attack detection, energy-based prediction approach is deemed to be a suitable one. A statistical method, exponential moving average (EMA) model based replica detection is proposed to detect replica node attack based on energy consumption threshold in WSNs. The difference between actual and predicted energy consumption exceeding the threshold level is considered as malicious. In this paper, future energy drop of a sensor node is forecasted using statistical measure instead of probabilistic method. In EMA model, the transition from higher power consuming state (active state) to lower power consuming states (sleep and sense states) is controlled by a fixed schedule. The accumulated average time of the node was in any state in the past is used to estimate the time duration of a node that spends in that state. Unlike Markov Model, the estimations of energy are made periodically. By this, computational overhead on the microcontroller of the sensor is greatly reduced in EMA approach. The simulation results taken using TRM simulator shows that choosing the threshold value which is neither too large nor too small results in optimum level of detection accuracy and lifetime of the network.

     

Driver fatigue detection based on deeply-learned facial expression representation

Driver fatigue detection is a significant application in smart cars. In order to improve the accuracy and timeliness of driver fatigue detection, a fatigue detection algorithm based on deeply-learned facial expression analysis is proposed. Specifically, the face key point detection model is first trained by multi block local binary patterns (MB-LBP) and Adaboost classifier. Subsequently, the eyes and mouth state are detected by using the trained model to detect the 24 facial features. Afterwards, we calculate the number of two parameters that can describe the driver's fatigue state and the proportion of the closed eye time within the unit time (PERCLOS) and yawning frequency. Finally, the fuzzy inference system is utilized to deduce the driver's fatigue state (normal, slight fatigue, severe fatigue). Experimental results show that the proposed algorithm can detect driver fatigue degree quickly and accurately.     

VRLA battery rapid charging under stress management

This paper discusses an approach for closed-loop charge control of valve-regulated lead-acid (VRLA) batteries used in telecommunication standby power applications. This is an alternative to conventional preprogrammed, configuration-driven charge control. The developed approach uses the battery's response to the supplied charge to control the recharge process. In this way, only the energy that can be absorbed by the battery in the desired recharge operation is dealt with. Hence, excessive energy that causes battery stress, or lack of energy that slows the charging process is avoided. Two main sources of recharge stress are identified: (a) thermal stress, and (b) charge saturation stress. A charge control algorithm based on thermal management and charge saturation avoidance is suggested. The algorithm utilizes a fuzzy state-of-charge estimation model that derives the state of charge from real-time parameters. This in turn calculates the maximum recharge rate that maintains the battery's thermal rise within permissible limits.     

A new method based on Fiber Bragg grating sensor for the milling force measurement

Milling force is an important parameter to describe the mechanical processing chip removal process, and it has a direct influence on generation of heat, tool wear or failure, quality of machined surface and accuracy of the work piece. Its accurate measurement is a significant basis for judging process state and improving the reliability of machining system. In this study, through analyzing the variation rule of ring diameter, a new method that using Fiber Bragg grating sensors and variation rule of ring diameter to measure the milling force has been proposed, and the basic structure of annulus has also been designed. A dynamometer has also been constructed, and the preliminary verification test was done. Through the analysis of experimental data, the dynamometer based on annulus elastic body can be used in milling force test, and it owns high sensitivity.     

A unified approach to position and force control by fuzzy logic

This paper proposes a unified control strategy of position and force. The authors' technique is based on impedance control with fuzzy logic and realizes the smooth shift from position control to force control and vice versa. At first, a robust impedance controller based on a disturbance observer is shown, and the method to unify the position and the force control through one controller is described. Next, an algorithm to estimate the dynamic characteristics of the environment is shown, and a force tracking control using the estimated parameters is proposed. Finally, the unified control algorithm of position and force based on fuzzy logic is established. The validity of this method is confirmed by several experimental results     

基于模糊逻辑的智能群体编队移动控制方法

针对群体移动过程中,各个智能体因受力波动较 大,而导致群体运动振荡的问题,提 出了一种基于分布式人工势场法与模糊控制相结合的控制算法。通过分析群体运动过程中单 智能体的受力情况,建立耗散力对移动中振荡进行优化,减小群体移动中的振荡负面影响, 提高群体运动的一致性。建立保持力对编队稳定和保持进行优化,提高编队的稳定性。将优 化过后的合力作为模糊控制器的输入,通过强化学习适应环境变化,调节参数控制输出,实 现群体跟随移动控制。此外,给出了可自动调整大小的多种编队控制生成模型,实现群体编 队移动控制。仿真结果表明能 稳定、有效控制群体跟随移动,编队移动中能有效避障,可使 编队提升移动效率和保持拓扑稳定性。     

基于窗口参数估计的距离扩展目标CFAR检测

雷达在探测目标时,若目标尺寸远大于雷达的距离分辨率,会在距离维上呈现出距离扩展目标特性,采用传统的点目标检测方法因无法利用全部能量而造成检测性能下降。本文针对距离扩展目标检测问题,以外辐射源雷达为背景,首先建立了外辐射源雷达距离扩展目标回波模型,提出了基于最大广义信噪比准则的距离扩展窗口参数估计方法来预估距离扩展目标的位置与宽度;为进一步判断窗口内是否为目标,研究了距离扩展窗口内的模糊恒虚警率（CFAR）检测算法,仿真比较了基于不同积累准则对各类起伏目标的模糊CFAR检测性能,并与传统的二进制积累CFAR比较,表明在均匀噪声环境和多目标环境下,基于模糊代数积积累的模糊CFAR检测性能均最优。最后结合外辐射源雷达舰船目标探测实验的实测数据处理结果,证明了基于模糊代数积积累的模糊CFAR具有最优的检测性能。     

变论域模糊控制器在光伏系统MPPT中的应用研究

为了解决光伏发电系统非线性特点及一般模糊控制存在稳态误差的问题,将变论域模糊控制应用到光伏发电系统的MPPT（Maximum Power Point Tracking）中,介绍了变论域模糊控制伸缩因子的选取及控制器的设计,运用Matlab/Simulink建立基于BOOST转换电路的MPPT系统仿真模型。通过调节Boost电路开关管的占空比使光伏电池工作在最大功率点,并对该方法进行了仿真验证。仿真结果表明,变论域模糊控制能够改善光伏发电系统MPPT的稳定性,减小稳态误差与波动,提高太阳能的利用率。     

基于模糊控制的电动汽车再生制动系统的研究

再生制动能够降低能源的消耗量和延长电动汽车的行驶里程。它广泛的受到诸多学者的关注。本文提出了一个新颖的再生制动控制系统。该系统基于无刷直流电机的控制特性和电动汽车刹车时的制动特性,直流无刷电机采用传统的PID控制,刹车力采用模糊逻辑控制,刹车力矩可以由PID控制器实时的控制。通过Matlab/Simulink软件,仿真分析了电池的充电状态、制动力和直流侧线电流。实验和仿真结果均证实了在具有良好的刹车性能的前提下,该方法可以实现良好的再生制动性能和延长电动汽车的行驶里程,在工程上更加易于实现也具有更好的鲁棒性和更高的效率。     

Geographic Data Propagation in Location-Unaware Wireless Sensor Networks: A Two-Dimensional Random Walk Analysis

For wireless sensor networks with many locationunaware nodes, which can be modeled as a planar Poisson point process, we investigate a protocol, dubbed BeSpoken, which steers data transmissions along a straight path called a spoke. BeSpoken implements a simple, spatially recursive process, where a basic set of control packets and a data packet are exchanged repeatedly among daisy-chained relays that constitute the spoke. Hence, a data packet originated by the first relay makes a forward progress in the direction of the spoke. Despite the simplicity of the protocol engine, modeling the spoke process is a significant challenge. Bespoken directs data transmissions by randomly selecting relays to retransmit data packets from crescent-shaped areas along the spoke axis. The resulting random walk of the spoke hop sequence may be modeled as a two dimensional Markov process. Based on this model, we propose design rules for protocol parameters that minimize energy consumption while ensuring that spokes propagate far enough and have a limited wobble with respect to the spoke axis. The energy efficiency is demonstrated through simulations of the BeSpoken-based data search, and a comparison with the energy consumption of a search based on directed diffusion.     

Detection of tool deflection in milling by a sensory axis slide for machine tools

Previous publications regarding the project Gentelligent Components for Machine Tools of the Collaborative Research Center 653 presented the design of a new sensory z-slide for a 5-axis machining center. Equipped with several strain sensors, the new slide is able to feel the machining process by measuring the process forces and vibrations. Here, a challenge is the detection of mechanical strains in the slide without degrading its high global stiffness. The application of micro strain gages in small notches on the slide represents a promising approach for the improvement of the sensitivity as well as the integration of sensors into the slide. This paper presents the utilization of the sensing axis-slide in a manufacturing environment. For this purpose, a first prototype of the slide is build and integrated into a milling center DMG HSC 55 linear. In this test machine, the dynamic characteristics of the integrated slide are identified with frequency response function measurements. Based on force measurements with a dynamometer, force calibration matrices are computed to calculate the forces in the machine coordinate system at the tool center point from the measured strain signals during milling processes. The force sensing with the slide allows furthermore the identification of tool characteristics such as the static tool stiffness. This parameter is estimated from the ratio of the measured contact forces and the set collision distance when moving the tool smoothly into the work piece. The known tool stiffness enables the detection of the static tool deflection from the force signals during a milling process. To compare the detected tool deflection with the real tool deflection, reference measurements on the work piece are performed using a perthometer. For further monitoring applications of the tool deflection in more complex 2.5D milling an approach to transform the measured forces from the stationary machine coordinate system into the moving tool coordinate system is presented.     

智能控制在无人值守换热站中的应用

本文根据传统PID控制和模糊控制的优点基础上,设计基于PLc的自适应模糊-PID过程参数控制系统。并将其应用在换热站中的室外温度补偿、分时分段供热、供水量控制、供水温度控制及供水压力控制中。以偏差和偏差变化率作为输入,并根据被控系统不同工况变化的要求,通过修改P I D控制器的参数来获得满意的动态和静态的控制性能,以便达到减少能耗、提高效能及减少二次污染。     

Adaptive learning control of milling operations

An adaptive learning control of milling operations in order to improve productivity is presented. Basically, the proposed control system consists of two parts. A feedforward neural network is first developed to acquire the inverse-dynamics model of the controlled plant. Then, a fuzzy feedback mechanism is designed to perform an adaptive modification of connection weights for the feedforward neural network. Based on this control system, an on-line adjustment of feedrate to achieve a constant milling force under a variety of cutting conditions is shown.     

基于MSP430F449的悬挂运动控制系统设计

以低功耗MSP430F449单片机系统平台为控制核心,由步进电机控制模块、红外传感和人机交互3个功能部分组成.由MSP430F449实现相应算法产生不同状态的PWM波,以控制电机的运动,从而实现对画笔的控制.系统可通过键盘任意设置坐标点参数;控制质量大于100g的物体在仰角不大于100°的80 cm×100cm白板上做...     

Supervised adaptive PID control and timevarying fuzzy identifier of nonlinear dither systems

To enhance system robustness between system parameters and adaptation law, a supervised adaptive PID control scheme and time-varying fuzzy identifier of unknown nonlinear dither systems are proposed. The dither and the proposed time-varying term can obtain the smooth quality of plant for reducing the rule number of the fuzzy identifier. The supervised adaptive PID control law aims to pull the states back to the pre-specified state region and reduce overshoot towards an extra stabilising input.